The Digital Executive Podcast:

Ahikam Kaufman on Closing the Financial Data Gap with AI Innovation
Episode 1078 – July 2, 2025

Host: Brian (Coruzant Technologies)
Guest: Ahikam Kaufman (Co-founder & CEO, SafeBooks AI)

Episode Overview

In this concise, insight-rich episode, Ahikam Kaufman—veteran fintech leader and CEO of SafeBooks AI—discusses the persistent challenges of financial data governance in enterprises and how AI-driven solutions are poised to remedy them. Drawing from his deep experience at Intuit and multiple successful startups, Ahikam highlights the current limits of AI in finance, the evolving future of financial compliance, and practical steps for finance leaders to harness new technology. The conversation offers actionable insights on closing the finance data gap, reducing due diligence risks, and unlocking truly strategic roles for CFOs and finance teams.

Key Discussion Points & Insights

1. The Inspiration Behind SafeBooks AI

Timestamp: [01:17]

• Personal Motivation: Ahikam shares how decades in the "office of the CFO" revealed a serious gap between the volume of financial data and organizations' abilities to efficiently govern it, especially due to disparate systems.
• Industry Need: The complexity and sensitivity in finance demand absolute accuracy and up-to-date recordkeeping, but "the tools today are just not good enough" for handling large transactions across multiple systems.
• SafeBooks Origin: Internal solutions at companies like Intuit offered partial fixes, but Ahikam saw the potential to scale these learnings into a product available to the broader market.

“There’s a serious gap between the ability of the office on the CFO to deal with...the amounts of data they have to deal with between or across disparate systems.”
— Ahikam Kaufman [01:32]

2. AI's Emerging — and Cautious — Role in Financial Governance

Timestamp: [03:24]

• Slow Adoption: Contextualizes AI’s deeper penetration in marketing and sales versus the high selectiveness in finance, due to the sensitive, risk-averse nature of financial data.
• Challenges for AI in Finance: Current AI weaknesses (e.g., errors, hallucinations) pose higher risks in finance than other functions.
• Promise of AI: Despite skepticism, Ahikam argues that AI can revolutionize finance—chiefly by automating repetitive tasks, accelerating reporting, and freeing up human capital for decision-making, provided use cases are robust and well-validated.

“In the office of the CFO penetration of AI has been limited...the weaknesses of AI today and the vulnerabilities of AI impose a lesser risk to [other] functions.”
— Ahikam Kaufman [03:32]

3. AI in the Mergers & Acquisitions Due Diligence Process

Timestamp: [05:57]

• Current Pain Points: The due diligence phase, especially for buyers, is data-intensive and painstaking, with most resources devoted to pulling, collecting, and validating data.
• Potential Impact of AI: Automated data collection and processing can significantly speed up due diligence, highlight risk factors, and allow human teams to focus on high-value analysis.
• Resource Allocation: Ahikam observes that “maybe 60–70%” of the work—and cost—in diligence is pure data wrangling, an area ripe for AI-fueled transformation.

“Being able to navigate through mountains of data...and identify topics of discussion or risk could make the diligence process a lot easier.”
— Ahikam Kaufman [06:09]

4. The Next 5–10 Years: AI as the Finance Team’s Copilot

Timestamp: [08:03]

• Analogy: Autonomous Cars: Ahikam likens the evolution of AI in finance to autonomous driving—first comes data collection, then processing and limited assistance, ultimately leading to (partial) automation aligning with key workflows.
• Strategic Shift: Repetitive, high-volume tasks such as data booking, entry validation, reconciliation, and payroll checks will increasingly be automated, liberating finance professionals to focus on judgment, decision-making, and business support.
• Preparing for the Shift: The core requirement is building the “infrastructure to collect data, then...clean and arrange the data to prepare it for AI, and then run AI on top of it.”
• Ultimate Vision: Human expertise will move from process-heavy tasks to high-level strategic contributions as AI systems grow more reliable and integrated.

“Being able to bridge all the repetitive, heavily manual, to some extent boring and annoying tasks around data...and being able to focus on supporting the business, making accounting decisions, looking at the data and making business decisions. That’s what really we want finance people to focus on.”
— Ahikam Kaufman [11:18]

Notable Quotes & Memorable Moments

• “The penetration of AI has been limited...I’ve even heard customers talking about, ‘Please, I want this solution, I don’t want any AI!’”
  — Ahikam Kaufman [03:38]
• “I personally think that autonomous driving will be even better than human beings. The same will go for the future of finance.”
  — Ahikam Kaufman [09:47]
• “You mentioned using AI to prepare for AI. There are many processes to gather these mountains of data...with the end goal of automating all the repetitive tasks, leaving that strategic and decision making process to the humans.”
  — Brian, Host [11:41]

Important Segments and Timestamps

• [01:17] — Ahikam explains his inspiration for founding SafeBooks AI and the unmet needs in financial data governance.
• [03:24] — Discussion of AI’s cautious entry into the finance space; challenges and opportunities of AI-driven automation in finance.
• [05:57] — Insights into how AI could revolutionize the M&A due diligence process, reducing risk and effort.
• [08:03] — Vision for the finance function in the next decade: strategic, tech-assisted, and focused on value—using the analogy of autonomous vehicles.

Conclusion

Throughout the discussion, Ahikam Kaufman cements himself as both a realist and an optimist, probing the real limitations of today’s AI while outlining an actionable pathway to a more strategic, less manual finance function. His practical insights—rooted in hands-on experience—offer a compelling vision for AI in finance: one where trusted automation replaces drudgery and human talent is fully unleashed on business-critical decisions.